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Found 10 papers, 2 papers with code
Stecformer: Spatio-temporal Encoding Cascaded Transformer for Multivariate Long-term Time Series Forecasting
With the development of transformer-based methods, the performance of multivariate long-term time series forecasting has been significantly improved, however, the study of spatial features extracting in transformer-based model is rare and the consistency of different prediction periods is unsatisfactory due to the large span.
AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windows
Recently Transformer has shown good performance in several vision tasks due to its powerful modeling capabilities.
PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation
We expect that our PRSeg can promote the development of MLP-based decoder in semantic segmentation.
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification
A parallel hierarchy of local and global feature blocks is designed to efficiently extract local features and global representations at various semantic scales, with the flexibility to model at different scales and linear computational complexity relevant to image size.
TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors
Translation-based knowledge graph embedding has been one of the most important branches for knowledge representation learning since TransE came out.
A General 3D Non-Stationary Massive MIMO GBSM for 6G Communication Systems
A general three-dimensional (3D) non-stationary massive multiple-input multiple-output (MIMO) geometry-based stochastic model (GBSM) for the sixth generation (6G) communication systems is proposed in the paper.
DT-Net: A novel network based on multi-directional integrated convolution and threshold convolution
To solve the above problems, in this paper, we propose a novel end-to-end semantic segmentation algorithm, DT-Net, and use two new convolution strategies to better achieve end-to-end semantic segmentation of medical images.
A New Multiple Max-pooling Integration Module and Cross Multiscale Deconvolution Network Based on Image Semantic Segmentation
We use the output feature maps from the multiple max-pooling integration module as the input of the decoder network; the multiscale convolution of each submodule in the decoder network is cross-fused with the feature maps generated by the corresponding multiscale convolution in the encoder network.
Projected Estimation for Large-dimensional Matrix Factor Models
In this study, we propose a projection estimation method for large-dimensional matrix factor models with cross-sectionally spiked eigenvalues.
Methodology
Large-dimensional Factor Analysis without Moment Constraints
Large-dimensional factor model has drawn much attention in the big-data era, in order to reduce the dimensionality and extract underlying features using a few latent common factors.
Methodology
